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Meta-Learning

93 papers with code · Methodology

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

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Greatest papers with code

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

NeurIPS 2018 tensorflow/models

Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.

#2 best model for Semantic Segmentation on Cityscapes (using extra training data)

IMAGE CLASSIFICATION META-LEARNING SEMANTIC SEGMENTATION STREET SCENE PARSING

Meta-Learning Update Rules for Unsupervised Representation Learning

ICLR 2019 tensorflow/models

Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.

META-LEARNING UNSUPERVISED REPRESENTATION LEARNING

Learning to learn by gradient descent by gradient descent

NeurIPS 2016 deepmind/learning-to-learn

The move from hand-designed features to learned features in machine learning has been wildly successful.

META-LEARNING

NoRML: No-Reward Meta Learning

4 Mar 2019google-research/google-research

To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).

META-LEARNING

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

ICML 2017 cbfinn/maml

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

FEW-SHOT IMAGE CLASSIFICATION ONE-SHOT LEARNING

On First-Order Meta-Learning Algorithms

8 Mar 2018openai/supervised-reptile

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT LEARNING

Learning to Compare: Relation Network for Few-Shot Learning

CVPR 2018 floodsung/LearningToCompare_FSL

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

FEW-SHOT LEARNING META-LEARNING ZERO-SHOT LEARNING

Guiding Policies with Language via Meta-Learning

ICLR 2019 maximecb/gym-minigrid

However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task.

IMITATION LEARNING META-LEARNING

Prototypical Networks for Few-shot Learning

NeurIPS 2017 orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

FEW-SHOT IMAGE CLASSIFICATION ONE-SHOT LEARNING ZERO-SHOT LEARNING

Learning to reinforcement learn

17 Nov 2016awjuliani/Meta-RL

We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

META-LEARNING